Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations27428
Missing cells29554
Missing cells (%)5.1%
Duplicate rows410
Duplicate rows (%)1.5%
Total size in memory8.7 MiB
Average record size in memory330.8 B

Variable types

Categorical9
Numeric12

Alerts

tipo_propiedad has constant value "APARTAMENTO"Constant
Dataset has 410 (1.5%) duplicate rowsDuplicates
administracion is highly overall correlated with area and 5 other fieldsHigh correlation
area is highly overall correlated with administracion and 6 other fieldsHigh correlation
banos is highly overall correlated with administracion and 6 other fieldsHigh correlation
estrato is highly overall correlated with administracion and 5 other fieldsHigh correlation
habitaciones is highly overall correlated with area and 2 other fieldsHigh correlation
longitud is highly overall correlated with estratoHigh correlation
parqueaderos is highly overall correlated with administracion and 5 other fieldsHigh correlation
precio_arriendo is highly overall correlated with administracion and 5 other fieldsHigh correlation
precio_venta is highly overall correlated with administracion and 5 other fieldsHigh correlation
tipo_operacion is highly imbalanced (93.1%)Imbalance
jacuzzi is highly imbalanced (70.6%)Imbalance
piscina is highly imbalanced (51.6%)Imbalance
administracion has 2420 (8.8%) missing valuesMissing
precio_arriendo has 27111 (98.8%) missing valuesMissing
precio_venta is highly skewed (γ1 = 52.62397927)Skewed
area is highly skewed (γ1 = 44.28424103)Skewed
administracion is highly skewed (γ1 = 24.02686051)Skewed
parqueaderos has 3253 (11.9%) zerosZeros

Reproduction

Analysis started2025-11-23 17:45:47.939591
Analysis finished2025-11-23 17:45:56.811271
Duration8.87 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

tipo_propiedad
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
APARTAMENTO
27428 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters301708
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPARTAMENTO
2nd rowAPARTAMENTO
3rd rowAPARTAMENTO
4th rowAPARTAMENTO
5th rowAPARTAMENTO

Common Values

ValueCountFrequency (%)
APARTAMENTO27428
100.0%

Length

2025-11-23T12:45:56.840174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:56.866224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
apartamento27428
100.0%

Most occurring characters

ValueCountFrequency (%)
A82284
27.3%
T54856
18.2%
P27428
 
9.1%
R27428
 
9.1%
M27428
 
9.1%
E27428
 
9.1%
N27428
 
9.1%
O27428
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)301708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A82284
27.3%
T54856
18.2%
P27428
 
9.1%
R27428
 
9.1%
M27428
 
9.1%
E27428
 
9.1%
N27428
 
9.1%
O27428
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)301708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A82284
27.3%
T54856
18.2%
P27428
 
9.1%
R27428
 
9.1%
M27428
 
9.1%
E27428
 
9.1%
N27428
 
9.1%
O27428
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)301708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A82284
27.3%
T54856
18.2%
P27428
 
9.1%
R27428
 
9.1%
M27428
 
9.1%
E27428
 
9.1%
N27428
 
9.1%
O27428
 
9.1%

tipo_operacion
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
VENTA
27200 
VENTA Y ARRIENDO
 
228

Length

Max length16
Median length5
Mean length5.0914394
Min length5

Characters and Unicode

Total characters139648
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVENTA
2nd rowVENTA
3rd rowVENTA
4th rowVENTA
5th rowVENTA

Common Values

ValueCountFrequency (%)
VENTA27200
99.2%
VENTA Y ARRIENDO228
 
0.8%

Length

2025-11-23T12:45:56.897763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:56.925128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
venta27428
98.4%
y228
 
0.8%
arriendo228
 
0.8%

Most occurring characters

ValueCountFrequency (%)
E27656
19.8%
N27656
19.8%
A27656
19.8%
V27428
19.6%
T27428
19.6%
456
 
0.3%
R456
 
0.3%
Y228
 
0.2%
I228
 
0.2%
D228
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)139648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E27656
19.8%
N27656
19.8%
A27656
19.8%
V27428
19.6%
T27428
19.6%
456
 
0.3%
R456
 
0.3%
Y228
 
0.2%
I228
 
0.2%
D228
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E27656
19.8%
N27656
19.8%
A27656
19.8%
V27428
19.6%
T27428
19.6%
456
 
0.3%
R456
 
0.3%
Y228
 
0.2%
I228
 
0.2%
D228
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E27656
19.8%
N27656
19.8%
A27656
19.8%
V27428
19.6%
T27428
19.6%
456
 
0.3%
R456
 
0.3%
Y228
 
0.2%
I228
 
0.2%
D228
 
0.2%

precio_venta
Real number (ℝ)

High correlation  Skewed 

Distinct2755
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3755897 × 109
Minimum1000000
Maximum4.25 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:56.961119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile1.85 × 108
Q14.1 × 108
median7.1 × 108
Q31.35 × 109
95-th percentile3.1 × 109
Maximum4.25 × 1012
Range4.249999 × 1012
Interquartile range (IQR)9.4 × 108

Descriptive statistics

Standard deviation5.5900057 × 1010
Coefficient of variation (CV)23.531023
Kurtosis3067.6251
Mean2.3755897 × 109
Median Absolute Deviation (MAD)3.9 × 108
Skewness52.623979
Sum6.5157675 × 1013
Variance3.1248163 × 1021
MonotonicityNot monotonic
2025-11-23T12:45:57.011134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200000000402
 
1.5%
1100000000368
 
1.3%
650000000348
 
1.3%
1300000000345
 
1.3%
750000000335
 
1.2%
850000000330
 
1.2%
550000000322
 
1.2%
450000000321
 
1.2%
1400000000264
 
1.0%
1500000000247
 
0.9%
Other values (2745)24146
88.0%
ValueCountFrequency (%)
10000002
 
< 0.1%
11000006
< 0.1%
11300001
 
< 0.1%
11500001
 
< 0.1%
11600001
 
< 0.1%
11950001
 
< 0.1%
12000002
 
< 0.1%
12500001
 
< 0.1%
12800001
 
< 0.1%
13000002
 
< 0.1%
ValueCountFrequency (%)
4.25 × 10121
< 0.1%
3.3 × 10121
< 0.1%
3.24 × 10121
< 0.1%
3 × 10121
< 0.1%
2.9 × 10121
< 0.1%
2.8 × 10121
< 0.1%
2.1 × 10121
< 0.1%
1.98 × 10121
< 0.1%
1.8 × 10121
< 0.1%
1.45 × 10121
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct3845
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.25936
Minimum0
Maximum19621
Zeros19
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:57.060377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q169
median110
Q3180
95-th percentile327
Maximum19621
Range19621
Interquartile range (IQR)111

Descriptive statistics

Standard deviation234.76416
Coefficient of variation (CV)1.6387352
Kurtosis2817.4192
Mean143.25936
Median Absolute Deviation (MAD)49.18
Skewness44.284241
Sum3929317.8
Variance55114.212
MonotonicityNot monotonic
2025-11-23T12:45:57.116570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90286
 
1.0%
80277
 
1.0%
60273
 
1.0%
70260
 
0.9%
50243
 
0.9%
45220
 
0.8%
120218
 
0.8%
150217
 
0.8%
55215
 
0.8%
100213
 
0.8%
Other values (3835)25006
91.2%
ValueCountFrequency (%)
019
0.1%
16
 
< 0.1%
23
 
< 0.1%
4.931
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
112
 
< 0.1%
11.351
 
< 0.1%
132
 
< 0.1%
15.531
 
< 0.1%
ValueCountFrequency (%)
196211
< 0.1%
154131
< 0.1%
99991
< 0.1%
92421
< 0.1%
87261
< 0.1%
76851
< 0.1%
72631
< 0.1%
66121
< 0.1%
65501
< 0.1%
64341
< 0.1%

habitaciones
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7467551
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:57.155281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78047605
Coefficient of variation (CV)0.28414475
Kurtosis0.61057124
Mean2.7467551
Median Absolute Deviation (MAD)0
Skewness-0.3952681
Sum75338
Variance0.60914286
MonotonicityNot monotonic
2025-11-23T12:45:57.186317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
316426
59.9%
25833
 
21.3%
42685
 
9.8%
12192
 
8.0%
5291
 
1.1%
71
 
< 0.1%
ValueCountFrequency (%)
12192
 
8.0%
25833
 
21.3%
316426
59.9%
42685
 
9.8%
5291
 
1.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
5291
 
1.1%
42685
 
9.8%
316426
59.9%
25833
 
21.3%
12192
 
8.0%

banos
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8350591
Minimum0
Maximum6
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:57.217176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1673323
Coefficient of variation (CV)0.41174886
Kurtosis-0.75904916
Mean2.8350591
Median Absolute Deviation (MAD)1
Skewness0.32950519
Sum77760
Variance1.3626648
MonotonicityNot monotonic
2025-11-23T12:45:57.247956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
29408
34.3%
37317
26.7%
44725
17.2%
53031
 
11.1%
12932
 
10.7%
014
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
014
 
0.1%
12932
 
10.7%
29408
34.3%
37317
26.7%
44725
17.2%
53031
 
11.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
53031
 
11.1%
44725
17.2%
37317
26.7%
29408
34.3%
12932
 
10.7%
014
 
0.1%

administracion
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct3960
Distinct (%)15.8%
Missing2420
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean5083051.7
Minimum1
Maximum3.5 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:57.288739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile110000
Q1350000
median650000
Q31200000
95-th percentile2333650
Maximum3.5 × 109
Range3.5 × 109
Interquartile range (IQR)850000

Descriptive statistics

Standard deviation76683305
Coefficient of variation (CV)15.086076
Kurtosis709.54107
Mean5083051.7
Median Absolute Deviation (MAD)363000
Skewness24.026861
Sum1.2711696 × 1011
Variance5.8803293 × 1015
MonotonicityNot monotonic
2025-11-23T12:45:57.342918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000402
 
1.5%
1200000385
 
1.4%
1500000344
 
1.3%
1300000313
 
1.1%
1100000288
 
1.1%
450000273
 
1.0%
500000256
 
0.9%
600000238
 
0.9%
350000228
 
0.8%
10000226
 
0.8%
Other values (3950)22055
80.4%
(Missing)2420
 
8.8%
ValueCountFrequency (%)
15
< 0.1%
103
< 0.1%
333
< 0.1%
1401
 
< 0.1%
1501
 
< 0.1%
2001
 
< 0.1%
2251
 
< 0.1%
2501
 
< 0.1%
3001
 
< 0.1%
3101
 
< 0.1%
ValueCountFrequency (%)
35000000001
 
< 0.1%
33660000001
 
< 0.1%
29180000001
 
< 0.1%
24000000001
 
< 0.1%
21000000001
 
< 0.1%
20000000003
< 0.1%
19000000001
 
< 0.1%
18900000001
 
< 0.1%
18000000001
 
< 0.1%
17500000002
< 0.1%

parqueaderos
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7639638
Minimum-2
Maximum30
Zeros3253
Zeros (%)11.9%
Negative1
Negative (%)< 0.1%
Memory size428.6 KiB
2025-11-23T12:45:57.381924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum30
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.091402
Coefficient of variation (CV)0.61872133
Kurtosis15.860861
Mean1.7639638
Median Absolute Deviation (MAD)1
Skewness0.91661732
Sum48382
Variance1.1911584
MonotonicityNot monotonic
2025-11-23T12:45:57.413395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
210444
38.1%
17970
29.1%
33540
 
12.9%
03253
 
11.9%
42219
 
8.1%
-21
 
< 0.1%
301
 
< 0.1%
ValueCountFrequency (%)
-21
 
< 0.1%
03253
 
11.9%
17970
29.1%
210444
38.1%
33540
 
12.9%
42219
 
8.1%
301
 
< 0.1%
ValueCountFrequency (%)
301
 
< 0.1%
42219
 
8.1%
33540
 
12.9%
210444
38.1%
17970
29.1%
03253
 
11.9%
-21
 
< 0.1%

estrato
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.8681956
Minimum0
Maximum6
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:57.443191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2144534
Coefficient of variation (CV)0.24946685
Kurtosis-0.52243371
Mean4.8681956
Median Absolute Deviation (MAD)1
Skewness-0.72093443
Sum133520
Variance1.4748971
MonotonicityNot monotonic
2025-11-23T12:45:57.474681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
611985
43.7%
45752
21.0%
55315
19.4%
33370
 
12.3%
2919
 
3.4%
179
 
0.3%
07
 
< 0.1%
(Missing)1
 
< 0.1%
ValueCountFrequency (%)
07
 
< 0.1%
179
 
0.3%
2919
 
3.4%
33370
 
12.3%
45752
21.0%
55315
19.4%
611985
43.7%
ValueCountFrequency (%)
611985
43.7%
55315
19.4%
45752
21.0%
33370
 
12.3%
2919
 
3.4%
179
 
0.3%
07
 
< 0.1%

antiguedad
Categorical

Distinct8
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size1.9 MiB
MAS DE 20 ANOS
9729 
ENTRE 10 Y 20 ANOS
7469 
ENTRE 0 Y 5 ANOS
4586 
ENTRE 5 Y 10 ANOS
4431 
REMODELADO
977 
Other values (3)
 
226

Length

Max length18
Median length17
Mean length15.761689
Min length10

Characters and Unicode

Total characters432154
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENTRE 10 Y 20 ANOS
2nd rowMAS DE 20 ANOS
3rd rowENTRE 0 Y 5 ANOS
4th rowENTRE 10 Y 20 ANOS
5th rowMAS DE 20 ANOS

Common Values

ValueCountFrequency (%)
MAS DE 20 ANOS9729
35.5%
ENTRE 10 Y 20 ANOS7469
27.2%
ENTRE 0 Y 5 ANOS4586
16.7%
ENTRE 5 Y 10 ANOS4431
16.2%
REMODELADO977
 
3.6%
SOBRE PLANOS107
 
0.4%
EN CONSTRUCCION101
 
0.4%
PARA ESTRENAR18
 
0.1%
(Missing)10
 
< 0.1%

Length

2025-11-23T12:45:57.519268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:57.558481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
anos26215
21.4%
2017198
14.0%
y16486
13.4%
entre16486
13.4%
1011900
9.7%
de9729
 
7.9%
mas9729
 
7.9%
59017
 
7.3%
04586
 
3.7%
remodelado977
 
0.8%
Other values (6)452
 
0.4%

Most occurring characters

ValueCountFrequency (%)
95357
22.1%
E44899
10.4%
N43129
10.0%
A37082
 
8.6%
S36277
 
8.4%
033684
 
7.8%
O28585
 
6.6%
R17725
 
4.1%
217198
 
4.0%
T16605
 
3.8%
Other values (11)61613
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)432154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
95357
22.1%
E44899
10.4%
N43129
10.0%
A37082
 
8.6%
S36277
 
8.4%
033684
 
7.8%
O28585
 
6.6%
R17725
 
4.1%
217198
 
4.0%
T16605
 
3.8%
Other values (11)61613
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)432154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
95357
22.1%
E44899
10.4%
N43129
10.0%
A37082
 
8.6%
S36277
 
8.4%
033684
 
7.8%
O28585
 
6.6%
R17725
 
4.1%
217198
 
4.0%
T16605
 
3.8%
Other values (11)61613
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)432154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
95357
22.1%
E44899
10.4%
N43129
10.0%
A37082
 
8.6%
S36277
 
8.4%
033684
 
7.8%
O28585
 
6.6%
R17725
 
4.1%
217198
 
4.0%
T16605
 
3.8%
Other values (11)61613
14.3%

latitud
Real number (ℝ)

Distinct16430
Distinct (%)59.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6889077
Minimum4.4686294
Maximum4.8191967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:57.609454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.4686294
5-th percentile4.623
Q14.6654514
median4.6929593
Q34.7165185
95-th percentile4.7473183
Maximum4.8191967
Range0.3505673
Interquartile range (IQR)0.051067125

Descriptive statistics

Standard deviation0.038082369
Coefficient of variation (CV)0.0081217998
Kurtosis1.6727205
Mean4.6889077
Median Absolute Deviation (MAD)0.02522255
Skewness-0.68013649
Sum128607.36
Variance0.0014502669
MonotonicityNot monotonic
2025-11-23T12:45:57.659404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.706110
 
0.4%
4.694107
 
0.4%
4.69384
 
0.3%
4.67183
 
0.3%
4.6682
 
0.3%
4.68982
 
0.3%
4.69781
 
0.3%
4.779
 
0.3%
4.67274
 
0.3%
4.70374
 
0.3%
Other values (16420)26572
96.9%
ValueCountFrequency (%)
4.46862941
 
< 0.1%
4.4721324171
 
< 0.1%
4.4731056351
 
< 0.1%
4.4731062481
 
< 0.1%
4.47310731
 
< 0.1%
4.47315261
 
< 0.1%
4.4732679124
< 0.1%
4.4732814711
 
< 0.1%
4.4733221191
 
< 0.1%
4.47347162
< 0.1%
ValueCountFrequency (%)
4.81919671
< 0.1%
4.81818771
< 0.1%
4.81753061
< 0.1%
4.81564241
< 0.1%
4.80150651
< 0.1%
4.8003712
< 0.1%
4.7998361
< 0.1%
4.799391
< 0.1%
4.7991
< 0.1%
4.7981
< 0.1%

longitud
Real number (ℝ)

High correlation 

Distinct9217
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.062875
Minimum-74.213645
Maximum-74.014
Zeros0
Zeros (%)0.0%
Negative27428
Negative (%)100.0%
Memory size428.6 KiB
2025-11-23T12:45:57.708219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.213645
5-th percentile-74.138829
Q1-74.06859
median-74.05199
Q3-74.043106
95-th percentile-74.0283
Maximum-74.014
Range0.19964519
Interquartile range (IQR)0.025484

Descriptive statistics

Standard deviation0.033925579
Coefficient of variation (CV)-0.00045806457
Kurtosis3.0748307
Mean-74.062875
Median Absolute Deviation (MAD)0.01102
Skewness-1.804319
Sum-2031396.5
Variance0.0011509449
MonotonicityNot monotonic
2025-11-23T12:45:57.769292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.049276
 
1.0%
-74.052200
 
0.7%
-74.051190
 
0.7%
-74.047188
 
0.7%
-74.05182
 
0.7%
-74.045175
 
0.6%
-74.048171
 
0.6%
-74.043167
 
0.6%
-74.046163
 
0.6%
-74.053149
 
0.5%
Other values (9207)25567
93.2%
ValueCountFrequency (%)
-74.213645191
 
< 0.1%
-74.213581
 
< 0.1%
-74.213458231
 
< 0.1%
-74.212851761
 
< 0.1%
-74.212830973
< 0.1%
-74.21264371
 
< 0.1%
-74.212416572
< 0.1%
-74.212351
 
< 0.1%
-74.2121661
 
< 0.1%
-74.212091
 
< 0.1%
ValueCountFrequency (%)
-74.0141
 
< 0.1%
-74.0161
 
< 0.1%
-74.016291
 
< 0.1%
-74.0171
 
< 0.1%
-74.0184
< 0.1%
-74.018521
 
< 0.1%
-74.0193
< 0.1%
-74.021
 
< 0.1%
-74.020431
 
< 0.1%
-74.020791
 
< 0.1%

precio_arriendo
Real number (ℝ)

High correlation  Missing 

Distinct120
Distinct (%)37.9%
Missing27111
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean1.0498864 × 108
Minimum740000
Maximum1.8 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:57.817743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum740000
5-th percentile1754000
Q15500000
median8000000
Q313000000
95-th percentile22680000
Maximum1.8 × 1010
Range1.799926 × 1010
Interquartile range (IQR)7500000

Descriptive statistics

Standard deviation1.1163288 × 109
Coefficient of variation (CV)10.632853
Kurtosis218.08295
Mean1.0498864 × 108
Median Absolute Deviation (MAD)3900000
Skewness14.283536
Sum3.32814 × 1010
Variance1.24619 × 1018
MonotonicityNot monotonic
2025-11-23T12:45:57.869214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800000021
 
0.1%
1200000019
 
0.1%
1500000014
 
0.1%
550000013
 
< 0.1%
700000012
 
< 0.1%
1000000012
 
< 0.1%
900000010
 
< 0.1%
110000008
 
< 0.1%
65000008
 
< 0.1%
200000007
 
< 0.1%
Other values (110)193
 
0.7%
(Missing)27111
98.8%
ValueCountFrequency (%)
7400001
< 0.1%
7800001
< 0.1%
8500002
< 0.1%
9000001
< 0.1%
10000001
< 0.1%
10500001
< 0.1%
11000001
< 0.1%
11500001
< 0.1%
12000001
< 0.1%
13000001
< 0.1%
ValueCountFrequency (%)
1.8 × 10101
 
< 0.1%
80000000001
 
< 0.1%
22000000001
 
< 0.1%
21000000001
 
< 0.1%
350000002
< 0.1%
300000002
< 0.1%
261000001
 
< 0.1%
250000004
< 0.1%
236000001
 
< 0.1%
230000002
< 0.1%

jacuzzi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
0.0
26005 
1.0
 
1421

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82278
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.026005
94.8%
1.01421
 
5.2%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:57.914333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:57.945115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.026005
94.8%
1.01421
 
5.2%

Most occurring characters

ValueCountFrequency (%)
053431
64.9%
.27426
33.3%
11421
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
053431
64.9%
.27426
33.3%
11421
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
053431
64.9%
.27426
33.3%
11421
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
053431
64.9%
.27426
33.3%
11421
 
1.7%

gimnasio
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
0.0
18559 
1.0
8867 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82278
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.018559
67.7%
1.08867
32.3%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:57.978007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:58.001300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.018559
67.7%
1.08867
32.3%

Most occurring characters

ValueCountFrequency (%)
045985
55.9%
.27426
33.3%
18867
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
045985
55.9%
.27426
33.3%
18867
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
045985
55.9%
.27426
33.3%
18867
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
045985
55.9%
.27426
33.3%
18867
 
10.8%

ascensor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
1.0
17876 
0.0
9550 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82278
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.017876
65.2%
0.09550
34.8%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:58.032675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:58.057586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.017876
65.2%
0.09550
34.8%

Most occurring characters

ValueCountFrequency (%)
036976
44.9%
.27426
33.3%
117876
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
036976
44.9%
.27426
33.3%
117876
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
036976
44.9%
.27426
33.3%
117876
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
036976
44.9%
.27426
33.3%
117876
21.7%

conjunto_cerrado
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
0.0
15582 
1.0
11844 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82278
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.015582
56.8%
1.011844
43.2%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:58.091339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:58.115764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.015582
56.8%
1.011844
43.2%

Most occurring characters

ValueCountFrequency (%)
043008
52.3%
.27426
33.3%
111844
 
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
043008
52.3%
.27426
33.3%
111844
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
043008
52.3%
.27426
33.3%
111844
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
043008
52.3%
.27426
33.3%
111844
 
14.4%

piscina
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
0.0
24552 
1.0
2874 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82278
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.024552
89.5%
1.02874
 
10.5%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:58.145941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:58.169729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.024552
89.5%
1.02874
 
10.5%

Most occurring characters

ValueCountFrequency (%)
051978
63.2%
.27426
33.3%
12874
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
051978
63.2%
.27426
33.3%
12874
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
051978
63.2%
.27426
33.3%
12874
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
051978
63.2%
.27426
33.3%
12874
 
3.5%

vigilancia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
1.0
16435 
0.0
10991 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82278
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.016435
59.9%
0.010991
40.1%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:58.202051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:58.227994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.016435
59.9%
0.010991
40.1%

Most occurring characters

ValueCountFrequency (%)
038417
46.7%
.27426
33.3%
116435
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
038417
46.7%
.27426
33.3%
116435
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
038417
46.7%
.27426
33.3%
116435
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)82278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
038417
46.7%
.27426
33.3%
116435
20.0%

distancia_estacion_tm_m
Real number (ℝ)

Distinct18277
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1284.8775
Minimum4.94
Maximum7095.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:58.261389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.94
5-th percentile237.281
Q1586.9875
median1197.315
Q31731.255
95-th percentile2874.4525
Maximum7095.66
Range7090.72
Interquartile range (IQR)1144.2675

Descriptive statistics

Standard deviation843.56079
Coefficient of variation (CV)0.65653015
Kurtosis2.4180553
Mean1284.8775
Median Absolute Deviation (MAD)576.305
Skewness1.1060076
Sum35241619
Variance711594.81
MonotonicityNot monotonic
2025-11-23T12:45:58.308721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1399.9254
 
0.2%
749.4937
 
0.1%
21.4524
 
0.1%
3027.7522
 
0.1%
359.1122
 
0.1%
1704.0220
 
0.1%
2565.2620
 
0.1%
1473.8419
 
0.1%
2883.8419
 
0.1%
1518.7218
 
0.1%
Other values (18267)27173
99.1%
ValueCountFrequency (%)
4.941
 
< 0.1%
5.661
 
< 0.1%
17.21
 
< 0.1%
18.931
 
< 0.1%
19.244
 
< 0.1%
21.4524
0.1%
22.771
 
< 0.1%
23.871
 
< 0.1%
24.571
 
< 0.1%
26.931
 
< 0.1%
ValueCountFrequency (%)
7095.661
 
< 0.1%
6727.131
 
< 0.1%
6632.151
 
< 0.1%
6595.421
 
< 0.1%
6590.641
 
< 0.1%
6583.632
< 0.1%
6582.464
< 0.1%
6580.791
 
< 0.1%
6575.691
 
< 0.1%
6567.341
 
< 0.1%

distancia_parque_m
Real number (ℝ)

Distinct17855
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean817.93068
Minimum0.22
Maximum6168.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.6 KiB
2025-11-23T12:45:58.354604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile203.2
Q1489.9
median764.74
Q31092.015
95-th percentile1622.5405
Maximum6168.02
Range6167.8
Interquartile range (IQR)602.115

Descriptive statistics

Standard deviation444.42986
Coefficient of variation (CV)0.54335883
Kurtosis4.6144243
Mean817.93068
Median Absolute Deviation (MAD)301.49
Skewness1.0424088
Sum22434203
Variance197517.9
MonotonicityNot monotonic
2025-11-23T12:45:58.406191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
659.4756
 
0.2%
1242.5734
 
0.1%
847.2624
 
0.1%
1423.3423
 
0.1%
532.523
 
0.1%
491.4721
 
0.1%
445.1220
 
0.1%
800.5620
 
0.1%
1173.9619
 
0.1%
674.8419
 
0.1%
Other values (17845)27169
99.1%
ValueCountFrequency (%)
0.223
< 0.1%
4.661
 
< 0.1%
10.151
 
< 0.1%
12.511
 
< 0.1%
13.412
< 0.1%
13.764
< 0.1%
13.821
 
< 0.1%
15.011
 
< 0.1%
15.181
 
< 0.1%
16.421
 
< 0.1%
ValueCountFrequency (%)
6168.021
< 0.1%
5996.91
< 0.1%
5865.31
< 0.1%
5647.131
< 0.1%
4876.812
< 0.1%
4844.481
< 0.1%
4793.971
< 0.1%
4737.571
< 0.1%
4719.711
< 0.1%
4464.621
< 0.1%

Interactions

2025-11-23T12:45:55.726873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.152304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.649924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.767092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.275040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.798457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.332403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.850594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.545950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.067466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.628618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.178890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.771702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.188333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.690624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.804674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.315771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.841396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.370898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.888600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.586722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.111621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.690366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.219439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.817207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.232384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.736581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.850207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.356226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.889529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.418488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.936766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.633343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.155444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.731374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.266842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.862876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.271016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.782398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.894279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.401035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.934447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.463173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.980679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.674909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.201193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.770311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.313248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.911852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.315331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.825058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.934780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.439095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.978025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.502667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.019043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.717509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.247275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.817783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.361193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.958241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.355627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.451538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.980223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.483874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.020564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.548985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.066518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.763357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.294451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.869407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.405923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:56.000947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.397952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.498111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.020171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.523056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.065611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.588496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.107217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.806638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.342277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.921493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.451202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:56.045904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.435838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.538799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.063049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.565578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.108332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.632804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.151023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.850102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.384847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.969072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.497001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:56.093962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.478622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.584451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.102882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.615075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.154420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.678973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.373576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.892820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.430270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.011446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.542368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:56.138918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.518858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.632391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.149640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.669245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.200139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.719938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.417092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.934921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.472918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.051827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.590534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:56.420331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.562277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.674806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.189394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.714087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.240378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.764274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.457365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.976744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.517683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.094079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.635411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:56.465532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:49.604719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:50.719969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.234327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:51.754439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.285630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:52.805979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:53.501004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.019476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:54.567465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.134639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:55.681416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-23T12:45:58.451981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
administracionantiguedadareaascensorbanosconjunto_cerradodistancia_estacion_tm_mdistancia_parque_mestratogimnasiohabitacionesjacuzzilatitudlongitudparqueaderospiscinaprecio_arriendoprecio_ventatipo_operacionvigilancia
administracion1.0000.0000.8610.0100.7670.0000.0860.1220.7990.0130.3600.030-0.1180.4710.8080.0220.7690.8910.0000.012
antiguedad0.0001.0000.0000.1310.0930.1350.0660.0370.0900.3090.1420.0430.0840.0890.1090.2450.0000.0000.0520.048
area0.8610.0001.0000.0080.8570.0070.1170.0750.7010.0050.5810.023-0.0340.4020.8210.0210.8280.9150.0000.000
ascensor0.0100.1310.0081.0000.1990.0000.0690.0620.2890.2670.0570.0970.1540.2270.2040.1530.0600.0070.0200.190
banos0.7670.0930.8570.1991.0000.0940.1020.0930.6320.1830.5580.2510.0210.3710.7670.1400.7170.8220.1090.177
conjunto_cerrado0.0000.1350.0070.0000.0941.0000.1210.0300.1710.2420.0900.0880.2190.2060.0610.1590.0470.0120.0280.137
distancia_estacion_tm_m0.0860.0660.1170.0690.1020.1211.0000.1330.1040.0910.1170.0630.1370.4510.0870.0820.2570.0910.0430.069
distancia_parque_m0.1220.0370.0750.0620.0930.0300.1331.0000.1610.0280.0140.0330.0990.2250.1120.072-0.0370.0730.0360.033
estrato0.7990.0900.7010.2890.6320.1710.1040.1611.0000.1620.1690.112-0.0590.5220.6950.0830.4950.7750.0800.218
gimnasio0.0130.3090.0050.2670.1830.2420.0910.0280.1621.0000.0750.1660.1390.1420.1670.3740.0660.0110.0000.206
habitaciones0.3600.1420.5810.0570.5580.0900.1170.0140.1690.0751.0000.1610.1020.0770.4140.0800.5180.4130.0380.063
jacuzzi0.0300.0430.0230.0970.2510.0880.0630.0330.1120.1660.1611.0000.0740.0830.1470.1980.0000.0290.0310.084
latitud-0.1180.084-0.0340.1540.0210.2190.1370.099-0.0590.1390.1020.0741.0000.3180.0110.127-0.236-0.1190.0770.130
longitud0.4710.0890.4020.2270.3710.2060.4510.2250.5220.1420.0770.0830.3181.0000.4160.1350.2910.4210.0620.195
parqueaderos0.8080.1090.8210.2040.7670.0610.0870.1120.6950.1670.4140.1470.0110.4161.0000.0860.7530.8400.0680.163
piscina0.0220.2450.0210.1530.1400.1590.0820.0720.0830.3740.0800.1980.1270.1350.0861.0000.1250.0000.0040.073
precio_arriendo0.7690.0000.8280.0600.7170.0470.257-0.0370.4950.0660.5180.000-0.2360.2910.7530.1251.0000.9320.0960.069
precio_venta0.8910.0000.9150.0070.8220.0120.0910.0730.7750.0110.4130.029-0.1190.4210.8400.0000.9321.0000.0000.012
tipo_operacion0.0000.0520.0000.0200.1090.0280.0430.0360.0800.0000.0380.0310.0770.0620.0680.0040.0960.0001.0000.012
vigilancia0.0120.0480.0000.1900.1770.1370.0690.0330.2180.2060.0630.0840.1300.1950.1630.0730.0690.0120.0121.000

Missing values

2025-11-23T12:45:56.543186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-23T12:45:56.634094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-23T12:45:56.752084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

tipo_propiedadtipo_operacionprecio_ventaareahabitacionesbanosadministracionparqueaderosestratoantiguedadlatitudlongitudprecio_arriendojacuzzigimnasioascensorconjunto_cerradopiscinavigilanciadistancia_estacion_tm_mdistancia_parque_m
0APARTAMENTOVENTA339000000.076.03.02.0300000.01.03.0ENTRE 10 Y 20 ANOS4.746592-74.057571NaN0.00.00.01.00.00.01142.45426.09
1APARTAMENTOVENTA223000000.063.03.02.0NaN0.03.0MAS DE 20 ANOS4.730111-74.028170NaN0.00.00.00.00.00.02384.89472.47
2APARTAMENTOVENTA440898168.054.03.02.0305000.00.03.0ENTRE 0 Y 5 ANOS4.607378-74.082648NaN0.00.00.01.00.00.0232.22961.29
3APARTAMENTOVENTA158000000.043.02.02.0106600.00.02.0ENTRE 10 Y 20 ANOS4.740109-74.113675NaN0.00.00.01.00.00.02275.08539.98
4APARTAMENTOVENTA222800000.048.03.02.0151000.00.03.0MAS DE 20 ANOS4.763900-74.025280NaN0.00.00.01.00.00.02099.161661.14
5APARTAMENTOVENTA128900000.047.02.01.086500.00.02.0ENTRE 10 Y 20 ANOS4.632698-74.198111NaN0.00.00.01.00.00.02800.97404.78
6APARTAMENTOVENTA190000000.038.02.01.0178600.00.03.0ENTRE 5 Y 10 ANOS4.753458-74.093288NaN0.00.00.01.00.00.0746.74664.93
7APARTAMENTOVENTA149000000.037.02.01.0160000.00.03.0ENTRE 10 Y 20 ANOS4.759587-74.100971NaN0.00.00.01.00.00.01602.071309.04
8APARTAMENTOVENTA350000000.051.01.02.0NaN1.04.0ENTRE 5 Y 10 ANOS4.603247-74.118735NaN0.01.00.01.00.00.0944.98396.59
11APARTAMENTOVENTA480000000.070.03.02.0NaN1.04.0ENTRE 0 Y 5 ANOS4.634184-74.092137NaN0.00.00.00.00.00.0271.23601.46
tipo_propiedadtipo_operacionprecio_ventaareahabitacionesbanosadministracionparqueaderosestratoantiguedadlatitudlongitudprecio_arriendojacuzzigimnasioascensorconjunto_cerradopiscinavigilanciadistancia_estacion_tm_mdistancia_parque_m
43003APARTAMENTOVENTA4.980000e+0884.003.02.0379000.01.04.0ENTRE 10 Y 20 ANOS4.754657-74.037231NaN0.01.00.01.00.00.0975.62108.84
43004APARTAMENTOVENTA1.941300e+0847.002.01.0173999.00.03.0ENTRE 5 Y 10 ANOS4.605351-74.149984NaN0.00.00.01.00.00.01175.50232.86
43005APARTAMENTOVENTA1.370000e+0847.003.01.062000.00.02.0ENTRE 5 Y 10 ANOS4.628769-74.212831NaN0.00.00.01.00.00.04408.60905.81
43006APARTAMENTOVENTA1.210000e+0835.002.01.076200.00.02.0ENTRE 10 Y 20 ANOS4.648645-74.171201NaN0.00.00.01.00.00.01776.06876.37
43007APARTAMENTOVENTA1.300000e+0837.002.01.077000.00.02.0ENTRE 10 Y 20 ANOS4.641467-74.186482NaN0.00.00.01.00.00.02004.86301.87
43008APARTAMENTOVENTA1.900000e+0849.003.02.0204624.00.02.0ENTRE 10 Y 20 ANOS4.754530-74.080902NaN0.01.00.01.00.00.01712.71198.03
43009APARTAMENTOVENTA3.220000e+0866.003.02.0277400.01.04.0ENTRE 10 Y 20 ANOS4.742568-74.092140NaN0.00.00.01.00.00.0120.16631.25
43010APARTAMENTOVENTA3.300000e+0890.003.02.0112000.00.04.0MAS DE 20 ANOS4.630547-74.079590NaN0.00.00.00.00.00.033.211211.62
43011APARTAMENTOVENTA1.280000e+09157.003.03.01050000.03.06.0MAS DE 20 ANOS4.702636-74.027180NaN0.00.01.00.00.00.03005.881269.56
43012APARTAMENTOVENTA4.980000e+0884.343.02.0287000.01.04.0ENTRE 10 Y 20 ANOS4.620531-74.130990NaN0.00.01.00.01.00.01022.07648.01

Duplicate rows

Most frequently occurring

tipo_propiedadtipo_operacionprecio_ventaareahabitacionesbanosadministracionparqueaderosestratoantiguedadlatitudlongitudprecio_arriendojacuzzigimnasioascensorconjunto_cerradopiscinavigilanciadistancia_estacion_tm_mdistancia_parque_m# duplicates
231APARTAMENTOVENTA7.200000e+0890.003.03.0450000.01.04.0ENTRE 10 Y 20 ANOS4.617153-74.070410NaN0.01.01.01.00.01.0201.15555.4310
398APARTAMENTOVENTA3.350000e+09342.253.04.02040000.04.06.0ENTRE 10 Y 20 ANOS4.651000-74.052000NaN0.01.01.00.00.01.01296.3948.798
107APARTAMENTOVENTA4.000000e+0869.001.01.0420000.02.05.0REMODELADO4.641988-74.056850NaN0.00.01.01.00.01.01000.501088.507
194APARTAMENTOVENTA5.700000e+0889.003.02.0262000.01.04.0ENTRE 10 Y 20 ANOS4.658000-74.120000NaN0.01.01.01.01.01.01320.95567.296
242APARTAMENTOVENTA7.550000e+08102.003.02.0440000.02.04.0ENTRE 10 Y 20 ANOS4.661000-74.122000NaN0.01.01.01.01.01.01316.74521.516
249APARTAMENTOVENTA7.950000e+08153.003.03.0851000.02.05.0ENTRE 10 Y 20 ANOS4.724365-74.062190NaN0.01.01.01.00.01.01242.06945.936
250APARTAMENTOVENTA7.950000e+08153.003.03.0851000.02.05.0ENTRE 10 Y 20 ANOS4.724790-74.063330NaN0.01.01.01.00.01.01294.301061.536
275APARTAMENTOVENTA8.700000e+08142.004.04.0725000.02.04.0ENTRE 10 Y 20 ANOS4.733916-74.076164NaN0.01.01.00.01.00.0546.41805.506
277APARTAMENTOVENTA8.900000e+08122.004.03.0795000.02.05.0MAS DE 20 ANOS4.687611-74.045560NaN0.00.01.01.00.00.01319.631839.986
300APARTAMENTOVENTA1.150000e+0997.451.02.01045000.02.06.0ENTRE 10 Y 20 ANOS4.664814-74.051170NaN0.00.01.00.00.00.01078.4797.896